An intervention which lowers low density lipoprotein cholesterol has been shown to lower coronary heart disease morbidity. Nevertheless, debate persists regarding the public health policy implications of this finding. One authoritative body (the NCEP expert panel) has promoted a potentially costly program for high blood cholesterol detection and treatment. The research begins with a cost-effectiveness analysis of the expert panel's program. The research then examines the cost, effectiveness, and cost-effectiveness implications of detection and treatment programs which deviate from NCEP protocols. Alternatives to be considered include case-finding protocols which vary the number and type of blood cholesterol measurement used to determine subsequent treatments. The research uses decision analytic methods to stimulate alternative detection and treatment programs. Normal probability methods calculate conditional probabilities for outcomes related to measurements of blood cholesterol during case finding and treatment. Markov simulations estimate quality adjusted life-expectancy and accumulate costs. Structured reviews of the biomedical literature provide estimates for most of the parameters required by the simulation. Secondary analyses of existing data bases (e.g. Hanes II) provide estimates for risk factor distributions in subgroups targeted for more or less intensive case-finding and treatment. Threshold and sensitivity analytic methods examine specific issues with public health policy relevance (e.g., the relationship between cholesterol treatment thresholds and cost effectiveness). Initially, the research accepts the Markov assumption (i.e., transition probabilities, such as the probability of a coronary heart disease event, depend only on the most recent state). Later, the research investigates alternative assumptions (i.e. transition probabilities depend on the duration of active treatment).